In the past, “advanced” lipoprotein test panels have typically included a lipoprotein measurement of average low-density lipoprotein (LDL) particle size as well as LDL particle number, the latter representing the concentration or quantity (in concentration units such as nmol/L) and the former representing the average size of the LDL particles (in nm units) making up the LDL in the sample. For example, in the NMR LipoProfile® lipoprotein panel report available from LipoScience, Inc., located in Raleigh, N.C., the average LDL particle size corresponds to the average size of a sample's total LDL particles, i.e., the average size of the combined small, intermediate and large LDL particles. Any one person can have LDL particles present in a continuum of different particle sizes. See www.liposcience.com and U.S. Pat. No. 6,576,471 for exemplary reports of particular lipoprotein subclass parameters, the contents of the patent are hereby incorporated by reference as if recited in full herein.
Generally stated, U.S. Pat. No. 4,933,844, entitled Measurement of Blood Lipoprotein Constituents by Analysis of Data Analysis of Data Acquired from an NMR Spectrometer to Otvos and U.S. Pat. No. 5,343,389, entitled Method and Apparatus for Measuring Classes and Subclasses of Lipoproteins, also to Otvos, describe NMR evaluation techniques that concurrently obtain and measure a plurality of different lipoprotein constituents in an in vitro blood plasma or serum sample. See also, U.S. Pat. No. 6,617,167, entitled Method Of Determining Presence And Concentration Of Lipoprotein X In Blood Plasma And Serum. The contents of all the above patents are hereby incorporated by reference as if recited in full herein. To evaluate the lipoproteins in a blood plasma and/or serum sample, the amplitudes of a plurality of NMR spectroscopy derived signals within a chemical shift region of the NMR spectrum are derived by deconvolution of the composite signal or spectrum and are compared to predetermined test criteria to evaluate a patient's risk of having or developing coronary artery or heart disease.
Referring to FIG. 1, it is noted that the constituents of certain subclasses of lipoproteins have overlapping signals. For example, LDL constituent values, shown for clarity as only two (L2 and L5) LDL subclass constituent values, when presented on a spectrum graph of signal intensity versus ppm, can overlap considerably. The overlapping nature of the signals can produce a regression matrix that is nearly singular. Conventional statistical evaluation methods that employ non-negative least squares techniques on nearly collinear data may have unstable and variable regression coefficients. See Myers, Raymond H., Classical and Modern Regression with Applications, (2d ed., Mass. PWS-Kent, 1990); Box et al., Statistics for Experimenters; An Introduction to Design, Data Analysis, and Model Building, (New York, Wiley, 1978).
More recently, sample evaluation methodologies have been developed which can provide increased resolution and/or increased reliability in measurements of discrete size segments or size categories of lipoprotein subclass parameters of interest, such as LDL subclass particle concentrations in a biosample of interest. See, e.g., U.S. patent application Ser. No. 10/691,103, entitled Methods, Systems and Computer Programs for Deconvolving the Spectral Contribution of Chemical Constituents With Overlapping Signals, the contents of which are hereby incorporated by reference as if recited in full herein.
While average LDL particle size and/or total LDL particle number can provide clinically useful information and successfully identify persons that are at risk for coronary heart disease (CHD) and/or coronary artery disease (CAD), this information may discount or suppress the actual predictive risk in some samples for some people. Further, measuring the content of LDL in an in vitro blood plasma or serum sample may not be representative of a person's true risk. In view of the foregoing, there remains a need to provide improved predictive models for assessing a person's risk of developing or having CHD.